AutoML Leaderboard
| Best model | name | model_type | metric_type | metric_value | train_time | single_prediction_time |
|---|---|---|---|---|---|---|
| 1_Default_LightGBM | LightGBM | rmse | 141.053 | 16.25 | 0.0267 | |
| 2_Default_Xgboost | Xgboost | rmse | 140.832 | 5.14 | 0.0168 | |
| 3_Default_CatBoost | CatBoost | rmse | 139.879 | 14.73 | 0.0096 | |
| 4_Default_NeuralNetwork | Neural Network | rmse | 151.076 | 12.06 | 0.0382 | |
| 5_Default_RandomForest | Random Forest | rmse | 166.996 | 12.15 | 0.0877 | |
| 10_LightGBM | LightGBM | rmse | 140.166 | 17.32 | 0.0185 | |
| 6_Xgboost | Xgboost | rmse | 142.295 | 5.25 | 0.0171 | |
| 14_CatBoost | CatBoost | rmse | 139.952 | 27.59 | 0.0098 | |
| 18_RandomForest | Random Forest | rmse | 165.693 | 9.61 | 0.086 | |
| 22_NeuralNetwork | Neural Network | rmse | 151.287 | 7.51 | 0.0299 | |
| 11_LightGBM | LightGBM | rmse | 141.572 | 6.21 | 0.0159 | |
| 7_Xgboost | Xgboost | rmse | 142.467 | 4.84 | 0.0168 | |
| 15_CatBoost | CatBoost | rmse | 140.663 | 17.59 | 0.0095 | |
| 19_RandomForest | Random Forest | rmse | 172.158 | 9.13 | 0.0831 | |
| 23_NeuralNetwork | Neural Network | rmse | 149.387 | 8.78 | 0.0284 | |
| 12_LightGBM | LightGBM | rmse | 140.962 | 13.91 | 0.0149 | |
| 8_Xgboost | Xgboost | rmse | 140.801 | 4.88 | 0.017 | |
| 16_CatBoost | CatBoost | rmse | 140.31 | 13.48 | 0.01 | |
| 20_RandomForest | Random Forest | rmse | 157.964 | 17.84 | 0.0963 | |
| 24_NeuralNetwork | Neural Network | rmse | 152.47 | 10.44 | 0.0285 | |
| 13_LightGBM | LightGBM | rmse | 141.67 | 15.34 | 0.0147 | |
| 9_Xgboost | Xgboost | rmse | 141.798 | 4.49 | 0.0166 | |
| 17_CatBoost | CatBoost | rmse | 141.953 | 8.08 | 0.0093 | |
| 21_RandomForest | Random Forest | rmse | 157.44 | 20.97 | 0.0832 | |
| 25_NeuralNetwork | Neural Network | rmse | 151.519 | 10.37 | 0.0291 | |
| 3_Default_CatBoost_GoldenFeatures | CatBoost | rmse | 139.949 | 15.29 | 0.0249 | |
| 14_CatBoost_GoldenFeatures | CatBoost | rmse | 139.627 | 30.77 | 0.0256 | |
| 10_LightGBM_GoldenFeatures | LightGBM | rmse | 140.169 | 17.71 | 0.0285 | |
| 26_CatBoost_GoldenFeatures | CatBoost | rmse | 139.644 | 52.43 | 0.0243 | |
| 27_CatBoost_GoldenFeatures | CatBoost | rmse | 139.887 | 18.29 | 0.0262 | |
| 28_CatBoost | CatBoost | rmse | 139.792 | 31.28 | 0.0104 | |
| 29_CatBoost | CatBoost | rmse | 140.22 | 9.62 | 0.0098 | |
| 30_LightGBM | LightGBM | rmse | 140.351 | 8.77 | 0.0154 | |
| 31_LightGBM_GoldenFeatures | LightGBM | rmse | 140.321 | 11.57 | 0.0286 | |
| 32_Xgboost | Xgboost | rmse | 140.555 | 5.2 | 0.0167 | |
| 33_Xgboost | Xgboost | rmse | 141.18 | 5.38 | 0.0165 | |
| 34_Xgboost | Xgboost | rmse | 140.469 | 5.23 | 0.0187 | |
| 35_Xgboost | Xgboost | rmse | 141.584 | 5.49 | 0.0167 | |
| 36_NeuralNetwork | Neural Network | rmse | 149.262 | 8.97 | 0.0301 | |
| 37_NeuralNetwork | Neural Network | rmse | 153.63 | 6.36 | 0.0282 | |
| 38_NeuralNetwork | Neural Network | rmse | 150.007 | 9.38 | 0.0302 | |
| 39_CatBoost_GoldenFeatures | CatBoost | rmse | 139.789 | 28.58 | 0.0287 | |
| the best | Ensemble | Ensemble | rmse | 138.929 | 1.41 | 0.2275 |
AutoML Performance
AutoML Performance Boxplot
Features Importance (Original Scale)
Scaled Features Importance (MinMax per Model)
Spearman Correlation of Models
Summary of 4_Default_NeuralNetwork
Neural Network
- n_jobs: -1
- dense_1_size: 32
- dense_2_size: 16
- learning_rate: 0.05
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
Optimized metric
rmse
Training time
11.8 seconds
Metric details:
| Metric | Score |
|---|---|
| MAE | 116.216 |
| MSE | 22823.9 |
| RMSE | 151.076 |
| R2 | 0.703061 |
| MAPE | 0.178454 |
Learning curves
Permutation-based Importance
True vs Predicted
Predicted vs Residuals
Summary of 29_CatBoost
CatBoost
- n_jobs: -1
- learning_rate: 0.2
- depth: 6
- rsm: 1
- loss_function: RMSE
- eval_metric: RMSE
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
Optimized metric
rmse
Training time
9.3 seconds
Metric details:
| Metric | Score |
|---|---|
| MAE | 107.333 |
| MSE | 19661.6 |
| RMSE | 140.22 |
| R2 | 0.744202 |
| MAPE | 0.168014 |
Learning curves
Permutation-based Importance
True vs Predicted
Predicted vs Residuals
Summary of 28_CatBoost
CatBoost
- n_jobs: -1
- learning_rate: 0.05
- depth: 6
- rsm: 1
- loss_function: RMSE
- eval_metric: RMSE
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
Optimized metric
rmse
Training time
30.9 seconds
Metric details:
| Metric | Score |
|---|---|
| MAE | 107.012 |
| MSE | 19541.7 |
| RMSE | 139.792 |
| R2 | 0.745762 |
| MAPE | 0.167949 |
Learning curves
Permutation-based Importance
True vs Predicted
Predicted vs Residuals
Summary of 17_CatBoost
CatBoost
- n_jobs: -1
- learning_rate: 0.2
- depth: 8
- rsm: 1.0
- loss_function: MAE
- eval_metric: RMSE
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
Optimized metric
rmse
Training time
7.8 seconds
Metric details:
| Metric | Score |
|---|---|
| MAE | 108.973 |
| MSE | 20150.7 |
| RMSE | 141.953 |
| R2 | 0.737839 |
| MAPE | 0.168768 |
Learning curves
Permutation-based Importance
True vs Predicted
Predicted vs Residuals
Summary of 16_CatBoost
CatBoost
- n_jobs: -1
- learning_rate: 0.1
- depth: 7
- rsm: 1.0
- loss_function: RMSE
- eval_metric: RMSE
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
Optimized metric
rmse
Training time
13.2 seconds
Metric details:
| Metric | Score |
|---|---|
| MAE | 107.416 |
| MSE | 19686.8 |
| RMSE | 140.31 |
| R2 | 0.743874 |
| MAPE | 0.167781 |
Learning curves
Permutation-based Importance
True vs Predicted
Predicted vs Residuals
Summary of Ensemble
Ensemble structure
| Model | Weight |
|---|---|
| 10_LightGBM | 1 |
| 11_LightGBM | 1 |
| 14_CatBoost_GoldenFeatures | 7 |
| 15_CatBoost | 1 |
| 27_CatBoost_GoldenFeatures | 3 |
| 29_CatBoost | 2 |
| 2_Default_Xgboost | 1 |
| 31_LightGBM_GoldenFeatures | 5 |
| 32_Xgboost | 1 |
| 33_Xgboost | 1 |
| 34_Xgboost | 3 |
| 3_Default_CatBoost | 3 |
| 3_Default_CatBoost_GoldenFeatures | 1 |
| 8_Xgboost | 1 |
Metric details:
| Metric | Score |
|---|---|
| MAE | 106.231 |
| MSE | 19301.1 |
| RMSE | 138.929 |
| R2 | 0.748892 |
| MAPE | 0.167287 |
Learning curves
True vs Predicted
Predicted vs Residuals
Summary of 26_CatBoost_GoldenFeatures
CatBoost
- n_jobs: -1
- learning_rate: 0.025
- depth: 8
- rsm: 0.8
- loss_function: RMSE
- eval_metric: RMSE
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
Optimized metric
rmse
Training time
52.0 seconds
Metric details:
| Metric | Score |
|---|---|
| MAE | 106.934 |
| MSE | 19500.5 |
| RMSE | 139.644 |
| R2 | 0.746299 |
| MAPE | 0.16814 |
Learning curves
Permutation-based Importance
True vs Predicted
Predicted vs Residuals
Summary of 31_LightGBM_GoldenFeatures
LightGBM
- n_jobs: -1
- objective: regression
- num_leaves: 15
- learning_rate: 0.1
- feature_fraction: 0.8
- bagging_fraction: 0.5
- min_data_in_leaf: 50
- metric: rmse
- custom_eval_metric_name: None
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
Optimized metric
rmse
Training time
11.2 seconds
Metric details:
| Metric | Score |
|---|---|
| MAE | 107.326 |
| MSE | 19690.1 |
| RMSE | 140.321 |
| R2 | 0.743832 |
| MAPE | 0.169257 |
Learning curves
Permutation-based Importance
True vs Predicted
Predicted vs Residuals
Summary of 38_NeuralNetwork
Neural Network
- n_jobs: -1
- dense_1_size: 64
- dense_2_size: 16
- learning_rate: 0.05
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
Optimized metric
rmse
Training time
9.1 seconds
Metric details:
| Metric | Score |
|---|---|
| MAE | 115.528 |
| MSE | 22502 |
| RMSE | 150.007 |
| R2 | 0.70725 |
| MAPE | 0.177053 |
Learning curves
Permutation-based Importance
True vs Predicted
Predicted vs Residuals
Summary of 25_NeuralNetwork
Neural Network
- n_jobs: -1
- dense_1_size: 32
- dense_2_size: 32
- learning_rate: 0.05
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
Optimized metric
rmse
Training time
10.1 seconds
Metric details:
| Metric | Score |
|---|---|
| MAE | 116.634 |
| MSE | 22957.9 |
| RMSE | 151.519 |
| R2 | 0.701317 |
| MAPE | 0.176767 |
Learning curves
Permutation-based Importance
True vs Predicted
Predicted vs Residuals
Summary of 36_NeuralNetwork
Neural Network
- n_jobs: -1
- dense_1_size: 32
- dense_2_size: 16
- learning_rate: 0.01
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
Optimized metric
rmse
Training time
8.7 seconds
Metric details:
| Metric | Score |
|---|---|
| MAE | 114.731 |
| MSE | 22279.2 |
| RMSE | 149.262 |
| R2 | 0.710147 |
| MAPE | 0.175333 |
Learning curves
Permutation-based Importance
True vs Predicted
Predicted vs Residuals
Summary of 10_LightGBM_GoldenFeatures
LightGBM
- n_jobs: -1
- objective: regression
- num_leaves: 15
- learning_rate: 0.05
- feature_fraction: 0.8
- bagging_fraction: 0.5
- min_data_in_leaf: 50
- metric: rmse
- custom_eval_metric_name: None
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
Optimized metric
rmse
Training time
17.3 seconds
Metric details:
| Metric | Score |
|---|---|
| MAE | 107.223 |
| MSE | 19647.2 |
| RMSE | 140.169 |
| R2 | 0.744389 |
| MAPE | 0.168656 |
Learning curves
Permutation-based Importance
True vs Predicted
Predicted vs Residuals
Summary of 11_LightGBM
LightGBM
- n_jobs: -1
- objective: regression
- num_leaves: 63
- learning_rate: 0.2
- feature_fraction: 0.5
- bagging_fraction: 1.0
- min_data_in_leaf: 10
- metric: rmse
- custom_eval_metric_name: None
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
Optimized metric
rmse
Training time
5.9 seconds
Metric details:
| Metric | Score |
|---|---|
| MAE | 108.506 |
| MSE | 20042.7 |
| RMSE | 141.572 |
| R2 | 0.739245 |
| MAPE | 0.169938 |
Learning curves
Permutation-based Importance
True vs Predicted
Predicted vs Residuals
Summary of 10_LightGBM
LightGBM
- n_jobs: -1
- objective: regression
- num_leaves: 15
- learning_rate: 0.05
- feature_fraction: 0.8
- bagging_fraction: 0.5
- min_data_in_leaf: 50
- metric: rmse
- custom_eval_metric_name: None
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
Optimized metric
rmse
Training time
17.0 seconds
Metric details:
| Metric | Score |
|---|---|
| MAE | 107.131 |
| MSE | 19646.6 |
| RMSE | 140.166 |
| R2 | 0.744398 |
| MAPE | 0.168613 |
Learning curves
Permutation-based Importance
True vs Predicted
Predicted vs Residuals
Summary of 3_Default_CatBoost
CatBoost
- n_jobs: -1
- learning_rate: 0.1
- depth: 6
- rsm: 1
- loss_function: RMSE
- eval_metric: RMSE
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
Optimized metric
rmse
Training time
14.4 seconds
Metric details:
| Metric | Score |
|---|---|
| MAE | 107.074 |
| MSE | 19566.2 |
| RMSE | 139.879 |
| R2 | 0.745444 |
| MAPE | 0.167898 |
Learning curves
Permutation-based Importance
True vs Predicted
Predicted vs Residuals
Summary of 8_Xgboost
Extreme Gradient Boosting (Xgboost)
- n_jobs: -1
- objective: reg:squarederror
- eta: 0.1
- max_depth: 7
- min_child_weight: 25
- subsample: 0.9
- colsample_bytree: 0.6
- eval_metric: rmse
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
Optimized metric
rmse
Training time
4.6 seconds
Metric details:
| Metric | Score |
|---|---|
| MAE | 107.682 |
| MSE | 19825 |
| RMSE | 140.801 |
| R2 | 0.742076 |
| MAPE | 0.168774 |
Learning curves
Permutation-based Importance
True vs Predicted
Predicted vs Residuals
Summary of 34_Xgboost
Extreme Gradient Boosting (Xgboost)
- n_jobs: -1
- objective: reg:squarederror
- eta: 0.075
- max_depth: 5
- min_child_weight: 1
- subsample: 1.0
- colsample_bytree: 1.0
- eval_metric: rmse
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
Optimized metric
rmse
Training time
5.0 seconds
Metric details:
| Metric | Score |
|---|---|
| MAE | 107.426 |
| MSE | 19731.4 |
| RMSE | 140.469 |
| R2 | 0.743294 |
| MAPE | 0.168258 |
Learning curves
Permutation-based Importance
True vs Predicted
Predicted vs Residuals
Summary of 21_RandomForest
Random Forest
- n_jobs: -1
- criterion: squared_error
- max_features: 0.6
- min_samples_split: 50
- max_depth: 6
- eval_metric_name: rmse
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
Optimized metric
rmse
Training time
20.7 seconds
Metric details:
| Metric | Score |
|---|---|
| MAE | 122.065 |
| MSE | 24787.2 |
| RMSE | 157.44 |
| R2 | 0.677518 |
| MAPE | 0.182079 |
Learning curves
Permutation-based Importance
True vs Predicted
Predicted vs Residuals
Summary of 14_CatBoost_GoldenFeatures
CatBoost
- n_jobs: -1
- learning_rate: 0.05
- depth: 8
- rsm: 0.8
- loss_function: RMSE
- eval_metric: RMSE
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
Optimized metric
rmse
Training time
30.4 seconds
Metric details:
| Metric | Score |
|---|---|
| MAE | 106.879 |
| MSE | 19495.6 |
| RMSE | 139.627 |
| R2 | 0.746362 |
| MAPE | 0.167978 |
Learning curves
Permutation-based Importance
True vs Predicted
Predicted vs Residuals
Summary of 9_Xgboost
Extreme Gradient Boosting (Xgboost)
- n_jobs: -1
- objective: reg:squarederror
- eta: 0.15
- max_depth: 8
- min_child_weight: 50
- subsample: 0.6
- colsample_bytree: 0.6
- eval_metric: rmse
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
Optimized metric
rmse
Training time
4.2 seconds
Metric details:
| Metric | Score |
|---|---|
| MAE | 108.48 |
| MSE | 20106.7 |
| RMSE | 141.798 |
| R2 | 0.738412 |
| MAPE | 0.169396 |
Learning curves
Permutation-based Importance
True vs Predicted
Predicted vs Residuals
Summary of 35_Xgboost
Extreme Gradient Boosting (Xgboost)
- n_jobs: -1
- objective: reg:squarederror
- eta: 0.075
- max_depth: 7
- min_child_weight: 1
- subsample: 1.0
- colsample_bytree: 1.0
- eval_metric: rmse
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
Optimized metric
rmse
Training time
5.2 seconds
Metric details:
| Metric | Score |
|---|---|
| MAE | 108.307 |
| MSE | 20046.1 |
| RMSE | 141.584 |
| R2 | 0.7392 |
| MAPE | 0.169085 |
Learning curves
Permutation-based Importance
True vs Predicted
Predicted vs Residuals
Summary of 20_RandomForest
Random Forest
- n_jobs: -1
- criterion: squared_error
- max_features: 1.0
- min_samples_split: 40
- max_depth: 6
- eval_metric_name: rmse
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
Optimized metric
rmse
Training time
17.5 seconds
Metric details:
| Metric | Score |
|---|---|
| MAE | 122.465 |
| MSE | 24952.7 |
| RMSE | 157.964 |
| R2 | 0.675366 |
| MAPE | 0.181925 |
Learning curves
Permutation-based Importance
True vs Predicted
Predicted vs Residuals
Summary of 22_NeuralNetwork
Neural Network
- n_jobs: -1
- dense_1_size: 32
- dense_2_size: 4
- learning_rate: 0.05
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
Optimized metric
rmse
Training time
7.2 seconds
Metric details:
| Metric | Score |
|---|---|
| MAE | 116.642 |
| MSE | 22887.9 |
| RMSE | 151.287 |
| R2 | 0.702228 |
| MAPE | 0.178601 |
Learning curves
Permutation-based Importance
True vs Predicted
Predicted vs Residuals
Summary of 39_CatBoost_GoldenFeatures
CatBoost
- n_jobs: -1
- learning_rate: 0.05
- depth: 7
- rsm: 0.8
- loss_function: RMSE
- eval_metric: RMSE
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
Optimized metric
rmse
Training time
28.1 seconds
Metric details:
| Metric | Score |
|---|---|
| MAE | 107.02 |
| MSE | 19540.9 |
| RMSE | 139.789 |
| R2 | 0.745773 |
| MAPE | 0.168249 |
Learning curves
Permutation-based Importance
True vs Predicted
Predicted vs Residuals
Summary of 23_NeuralNetwork
Neural Network
- n_jobs: -1
- dense_1_size: 64
- dense_2_size: 16
- learning_rate: 0.01
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
Optimized metric
rmse
Training time
8.5 seconds
Metric details:
| Metric | Score |
|---|---|
| MAE | 114.727 |
| MSE | 22316.4 |
| RMSE | 149.387 |
| R2 | 0.709664 |
| MAPE | 0.174903 |
Learning curves
Permutation-based Importance
True vs Predicted
Predicted vs Residuals
Summary of 27_CatBoost_GoldenFeatures
CatBoost
- n_jobs: -1
- learning_rate: 0.1
- depth: 8
- rsm: 0.8
- loss_function: RMSE
- eval_metric: RMSE
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
Optimized metric
rmse
Training time
17.9 seconds
Metric details:
| Metric | Score |
|---|---|
| MAE | 107.135 |
| MSE | 19568.3 |
| RMSE | 139.887 |
| R2 | 0.745416 |
| MAPE | 0.167774 |
Learning curves
Permutation-based Importance
True vs Predicted
Predicted vs Residuals
Summary of 37_NeuralNetwork
Neural Network
- n_jobs: -1
- dense_1_size: 16
- dense_2_size: 16
- learning_rate: 0.05
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
Optimized metric
rmse
Training time
6.1 seconds
Metric details:
| Metric | Score |
|---|---|
| MAE | 118.468 |
| MSE | 23602.1 |
| RMSE | 153.63 |
| R2 | 0.692937 |
| MAPE | 0.1798 |
Learning curves
Permutation-based Importance
True vs Predicted
Predicted vs Residuals
Summary of 24_NeuralNetwork
Neural Network
- n_jobs: -1
- dense_1_size: 64
- dense_2_size: 32
- learning_rate: 0.08
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
Optimized metric
rmse
Training time
10.2 seconds
Metric details:
| Metric | Score |
|---|---|
| MAE | 117.458 |
| MSE | 23247.2 |
| RMSE | 152.47 |
| R2 | 0.697553 |
| MAPE | 0.177513 |
Learning curves
Permutation-based Importance
True vs Predicted
Predicted vs Residuals
Summary of 3_Default_CatBoost_GoldenFeatures
CatBoost
- n_jobs: -1
- learning_rate: 0.1
- depth: 6
- rsm: 1
- loss_function: RMSE
- eval_metric: RMSE
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
Optimized metric
rmse
Training time
14.9 seconds
Metric details:
| Metric | Score |
|---|---|
| MAE | 107.174 |
| MSE | 19585.9 |
| RMSE | 139.949 |
| R2 | 0.745188 |
| MAPE | 0.168112 |
Learning curves
Permutation-based Importance
True vs Predicted
Predicted vs Residuals
Summary of 2_Default_Xgboost
Extreme Gradient Boosting (Xgboost)
- n_jobs: -1
- objective: reg:squarederror
- eta: 0.075
- max_depth: 6
- min_child_weight: 1
- subsample: 1.0
- colsample_bytree: 1.0
- eval_metric: rmse
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
Optimized metric
rmse
Training time
4.9 seconds
Metric details:
| Metric | Score |
|---|---|
| MAE | 107.739 |
| MSE | 19833.8 |
| RMSE | 140.832 |
| R2 | 0.741962 |
| MAPE | 0.168699 |
Learning curves
Permutation-based Importance
True vs Predicted
Predicted vs Residuals
Summary of 14_CatBoost
CatBoost
- n_jobs: -1
- learning_rate: 0.05
- depth: 8
- rsm: 0.8
- loss_function: RMSE
- eval_metric: RMSE
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
Optimized metric
rmse
Training time
27.3 seconds
Metric details:
| Metric | Score |
|---|---|
| MAE | 107.17 |
| MSE | 19586.7 |
| RMSE | 139.952 |
| R2 | 0.745177 |
| MAPE | 0.16823 |
Learning curves
Permutation-based Importance
True vs Predicted
Predicted vs Residuals
Summary of 15_CatBoost
CatBoost
- n_jobs: -1
- learning_rate: 0.1
- depth: 8
- rsm: 1.0
- loss_function: MAE
- eval_metric: RMSE
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
Optimized metric
rmse
Training time
17.3 seconds
Metric details:
| Metric | Score |
|---|---|
| MAE | 107.749 |
| MSE | 19786.2 |
| RMSE | 140.663 |
| R2 | 0.742582 |
| MAPE | 0.168133 |
Learning curves
Permutation-based Importance
True vs Predicted
Predicted vs Residuals
Summary of 33_Xgboost
Extreme Gradient Boosting (Xgboost)
- n_jobs: -1
- objective: reg:squarederror
- eta: 0.1
- max_depth: 8
- min_child_weight: 25
- subsample: 0.9
- colsample_bytree: 0.6
- eval_metric: rmse
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
Optimized metric
rmse
Training time
5.1 seconds
Metric details:
| Metric | Score |
|---|---|
| MAE | 108.065 |
| MSE | 19931.8 |
| RMSE | 141.18 |
| R2 | 0.740688 |
| MAPE | 0.16923 |
Learning curves
Permutation-based Importance
True vs Predicted
Predicted vs Residuals
Summary of 30_LightGBM
LightGBM
- n_jobs: -1
- objective: regression
- num_leaves: 15
- learning_rate: 0.1
- feature_fraction: 0.8
- bagging_fraction: 0.5
- min_data_in_leaf: 50
- metric: rmse
- custom_eval_metric_name: None
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
Optimized metric
rmse
Training time
8.4 seconds
Metric details:
| Metric | Score |
|---|---|
| MAE | 107.31 |
| MSE | 19698.4 |
| RMSE | 140.351 |
| R2 | 0.743723 |
| MAPE | 0.168915 |
Learning curves
Permutation-based Importance
True vs Predicted
Predicted vs Residuals
Summary of 6_Xgboost
Extreme Gradient Boosting (Xgboost)
- n_jobs: -1
- objective: reg:squarederror
- eta: 0.075
- max_depth: 8
- min_child_weight: 5
- subsample: 1.0
- colsample_bytree: 1.0
- eval_metric: rmse
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
Optimized metric
rmse
Training time
5.0 seconds
Metric details:
| Metric | Score |
|---|---|
| MAE | 109.035 |
| MSE | 20247.8 |
| RMSE | 142.295 |
| R2 | 0.736576 |
| MAPE | 0.169757 |
Learning curves
Permutation-based Importance
True vs Predicted
Predicted vs Residuals
Summary of 18_RandomForest
Random Forest
- n_jobs: -1
- criterion: squared_error
- max_features: 0.5
- min_samples_split: 20
- max_depth: 4
- eval_metric_name: rmse
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
Optimized metric
rmse
Training time
9.3 seconds
Metric details:
| Metric | Score |
|---|---|
| MAE | 129.203 |
| MSE | 27454.3 |
| RMSE | 165.693 |
| R2 | 0.64282 |
| MAPE | 0.189837 |
Learning curves
Permutation-based Importance
True vs Predicted
Predicted vs Residuals
Summary of 12_LightGBM
LightGBM
- n_jobs: -1
- objective: regression
- num_leaves: 63
- learning_rate: 0.05
- feature_fraction: 0.9
- bagging_fraction: 1.0
- min_data_in_leaf: 20
- metric: rmse
- custom_eval_metric_name: None
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
Optimized metric
rmse
Training time
13.6 seconds
Metric details:
| Metric | Score |
|---|---|
| MAE | 107.697 |
| MSE | 19870.3 |
| RMSE | 140.962 |
| R2 | 0.741488 |
| MAPE | 0.168839 |
Learning curves
Permutation-based Importance
True vs Predicted
Predicted vs Residuals
Summary of 13_LightGBM
LightGBM
- n_jobs: -1
- objective: regression
- num_leaves: 95
- learning_rate: 0.05
- feature_fraction: 1.0
- bagging_fraction: 1.0
- min_data_in_leaf: 10
- metric: rmse
- custom_eval_metric_name: None
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
Optimized metric
rmse
Training time
15.0 seconds
Metric details:
| Metric | Score |
|---|---|
| MAE | 108.368 |
| MSE | 20070.5 |
| RMSE | 141.67 |
| R2 | 0.738883 |
| MAPE | 0.169451 |
Learning curves
Permutation-based Importance
True vs Predicted
Predicted vs Residuals
Summary of 5_Default_RandomForest
Random Forest
- n_jobs: -1
- criterion: squared_error
- max_features: 0.9
- min_samples_split: 30
- max_depth: 4
- eval_metric_name: rmse
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
Optimized metric
rmse
Training time
11.8 seconds
Metric details:
| Metric | Score |
|---|---|
| MAE | 129.944 |
| MSE | 27887.8 |
| RMSE | 166.996 |
| R2 | 0.63718 |
| MAPE | 0.190035 |
Learning curves
Permutation-based Importance
True vs Predicted
Predicted vs Residuals
Summary of 7_Xgboost
Extreme Gradient Boosting (Xgboost)
- n_jobs: -1
- objective: reg:squarederror
- eta: 0.1
- max_depth: 8
- min_child_weight: 1
- subsample: 1.0
- colsample_bytree: 1.0
- eval_metric: rmse
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
Optimized metric
rmse
Training time
4.6 seconds
Metric details:
| Metric | Score |
|---|---|
| MAE | 109.139 |
| MSE | 20297 |
| RMSE | 142.467 |
| R2 | 0.735936 |
| MAPE | 0.169765 |
Learning curves
Permutation-based Importance
True vs Predicted
Predicted vs Residuals
Summary of 1_Default_LightGBM
LightGBM
- n_jobs: -1
- objective: regression
- num_leaves: 63
- learning_rate: 0.05
- feature_fraction: 0.9
- bagging_fraction: 0.9
- min_data_in_leaf: 10
- metric: rmse
- custom_eval_metric_name: None
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
Optimized metric
rmse
Training time
15.9 seconds
Metric details:
| Metric | Score |
|---|---|
| MAE | 107.869 |
| MSE | 19896 |
| RMSE | 141.053 |
| R2 | 0.741153 |
| MAPE | 0.169065 |
Learning curves
Permutation-based Importance
True vs Predicted
Predicted vs Residuals
Summary of 32_Xgboost
Extreme Gradient Boosting (Xgboost)
- n_jobs: -1
- objective: reg:squarederror
- eta: 0.1
- max_depth: 6
- min_child_weight: 25
- subsample: 0.9
- colsample_bytree: 0.6
- eval_metric: rmse
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
Optimized metric
rmse
Training time
4.9 seconds
Metric details:
| Metric | Score |
|---|---|
| MAE | 107.544 |
| MSE | 19755.7 |
| RMSE | 140.555 |
| R2 | 0.742978 |
| MAPE | 0.168492 |
Learning curves
Permutation-based Importance
True vs Predicted
Predicted vs Residuals
Summary of 19_RandomForest
Random Forest
- n_jobs: -1
- criterion: squared_error
- max_features: 0.7
- min_samples_split: 50
- max_depth: 3
- eval_metric_name: rmse
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
Optimized metric
rmse
Training time
8.8 seconds
Metric details:
| Metric | Score |
|---|---|
| MAE | 134.308 |
| MSE | 29638.3 |
| RMSE | 172.158 |
| R2 | 0.614405 |
| MAPE | 0.194642 |
Learning curves
Permutation-based Importance
True vs Predicted
Predicted vs Residuals
